CAJ | 학술논문

水质评估模型是进行水质规划、环境水污染控制和环境管理的有效工具. 利用遗传算法( GA)对支持向量机( SVM)分类算法的径向基核函数参数σ和错分惩罚因子C进行组合优化,建立进化支持向量机模型,并将该模型应用于水质评估中. 将该模型分别应用于松花江松原段、松花江哈尔滨段、黄河甘肃段和吉林桦甸关门砬子水库的真实数据上进行测试. 实验结果表明,提出的进化支持向量机水质评估模型在分类精度和泛化能力上较经典SVM方法都有所提高,表明了该方法的有效性.
수질평고모형시진행수질규화、배경수오염공제화배경관리적유효공구. 이용유전산법( GA)대지지향량궤( SVM)분류산법적경향기핵함수삼수σ화착분징벌인자C진행조합우화,건립진화지지향량궤모형,병장해모형응용우수질평고중. 장해모형분별응용우송화강송원단、송화강합이빈단、황하감숙단화길림화전관문립자수고적진실수거상진행측시. 실험결과표명,제출적진화지지향량궤수질평고모형재분류정도화범화능력상교경전SVM방법도유소제고,표명료해방법적유효성.
A water quality assessment model is an effective tool for water quality planning, environmental water pol-lution control and environment management. In this paper, an evolutionary support vector machine ( SVM) model is developed by using genetic algorithm ( GA) to combine and optimize the radial basis kernel function parameter σand error penalty factor C of a SVM algorithm. This model is then extended to water quality assessment. To test the effectiveness of the proposed method, it is applied to a simulation on real data of the Songyuan and Harbin sections of the Songhua River, the Gansu section of the Yellow River, and the Jilin Huadian Guanmenlizi water reservoir. Simulation results show that, compared with the classical SVM method, the classification accuracy and generaliza-tion ability of the evolutionary support vector machine model for water quality assessment are improved.